Partial epilepsies, originating in a specific brain region, affect about 60% of adults with epilepsy. Temporal lobe epilepsy (TLE) is the most prevalent subtype within this category, often necessitating surgical inter...Partial epilepsies, originating in a specific brain region, affect about 60% of adults with epilepsy. Temporal lobe epilepsy (TLE) is the most prevalent subtype within this category, often necessitating surgical intervention due to its refractoriness to antiepileptic drugs (AEDs). Hippocampal sclerosis, a common underlying pathology, often exacerbates the severity by introducing cognitive and emotional challenges. This review delves deeper into the cognitive profile of TLE, along with the risk factors for cognitive disorders, depression, and anxiety in this population.展开更多
Cognitive impairment is the most common complication in patients with temporal lobe epilepsy with hippocampal scle rosis.There is no effective treatment for cognitive impairment.Medial septum cholinergic neurons have ...Cognitive impairment is the most common complication in patients with temporal lobe epilepsy with hippocampal scle rosis.There is no effective treatment for cognitive impairment.Medial septum cholinergic neurons have been reported to be a potential target for controlling epileptic seizures in tempo ral lobe epile psy.However,their role in the cognitive impairment of temporal lobe epilepsy remains unclear.In this study,we found that patients with temporal lobe epile psy with hippocampal sclerosis had a low memory quotient and severe impairment in verbal memory,but had no impairment in nonverbal memory.The cognitive impairment was slightly correlated with reduced medial septum volume and medial septum-hippocampus tra cts measured by diffusion tensor imaging.In a mouse model of chronic temporal lobe epilepsy induced by kainic acid,the number of medial septum choline rgic neurons was reduced and acetylcholine release was reduced in the hippocampus.Furthermore,selective apoptosis of medial septum cholinergic neurons mimicked the cognitive deficits in epileptic mice,and activation of medial septum cholinergic neurons enhanced hippocampal acetylcholine release and restored cognitive function in both kainic acid-and kindling-induced epile psy models.These res ults suggest that activation of medial septum cholinergic neurons reduces cognitive deficits in temporal lobe epilepsy by increasing acetylcholine release via projections to the hippocampus.展开更多
Epilepsy frequently leads to cognitive dysfunction and approaches to treatment remain limited.Although regular exercise effectively improves learning and memory functions across multiple neurological diseases,its appl...Epilepsy frequently leads to cognitive dysfunction and approaches to treatment remain limited.Although regular exercise effectively improves learning and memory functions across multiple neurological diseases,its application in patients with epilepsy remains controversial.Here,we adopted a 14-day treadmill-exercise paradigm in a pilocarpine injection-induced mouse model of epilepsy.Cognitive assays confirmed the improvement of object and spatial memory after endurance training,and electrophysiological studies revealed the maintenance of hippocampal plasticity as a result of physical exercise.Investigations of the mechanisms underlying this effect revealed that exercise protected parvalbumin interneurons,probably via the suppression of neuroinflammation and improved integrity of blood-brain barrier.In summary,this work identified a previously unknown mechanism through which exercise improves cognitive rehabilitation in epilepsy.展开更多
In this paper,we investigate a spectrumsensing system in the presence of a satellite,where the satellite works as a sensing node.Considering the conventional energy detection method is sensitive to the noise uncertain...In this paper,we investigate a spectrumsensing system in the presence of a satellite,where the satellite works as a sensing node.Considering the conventional energy detection method is sensitive to the noise uncertainty,thus,a temporal convolutional network(TCN)based spectrum-sensing method is designed to eliminate the effect of the noise uncertainty and improve the performance of spectrum sensing,relying on the offline training and the online detection stages.Specifically,in the offline training stage,spectrum data captured by the satellite is sent to the TCN deployed on the gateway for training purpose.Moreover,in the online detection stage,the well trained TCN is utilized to perform real-time spectrum sensing,which can upgrade spectrum-sensing performance by exploiting the temporal features.Additionally,simulation results demonstrate that the proposed method achieves a higher probability of detection than that of the conventional energy detection(ED),the convolutional neural network(CNN),and deep neural network(DNN).Furthermore,the proposed method outperforms the CNN and the DNN in terms of a lower computational complexity.展开更多
Background: Mobility limitations and cognitive impairments which are common with ageing often coexist, causing a reduction in the levels of physical and mental activity and are prognostic of future adverse health even...Background: Mobility limitations and cognitive impairments which are common with ageing often coexist, causing a reduction in the levels of physical and mental activity and are prognostic of future adverse health events and falls. Consequently, multi-task training paradigms that simultaneously address both mobility and cognition benefit healthy ageing are important to consider in rehabilitation as well as primary prevention. Objectives: An exploratory RCT is being conducted to: a) describe the feasibility and acceptability of the study design and process, procedures, resources and management in two game-based dual-task training programs delivered in the community;b) to explore the lived experiences of the study participants who completed their respective exercise programs. A secondary objective is to obtain preliminary data on the therapeutic effectiveness of the two dual-task training programs. Methods: Thirty healthy older community dwelling participants aged 70 - 85 with previous history of falls will be recruited and randomized to either dual- task treadmill walking (experimental group) or dual-task recumbent bicycle (control group). Data analysis: The qualitative data will be analyzed by two investigators using a content analysis approach. For the quantitative data, outcome measures will be collected pre and post intervention and included measures to assess core balance, spatial-temporal gait variables, visual tracking and cognitive function, as well as, balance and gait analysis under dual-task conditions. Discussion: This research will demonstrate the feasibility of the dual-task training programs in the community, and demonstrate the system’s ability to improve targeted and integrated (dual-task) aspects of balance, mobility, gaze, and cognitive performance. A blended analysis of balance, mobility gaze and cognition will also contribute to a better understanding of the functional consequences of decline in physical and mental skills with age. Trial registration: This pilot clinical trial has been registered at ClinicalTrials.gov Protocol Registration System: NCT01940055.展开更多
Artificial intelligence, often referred to as AI, is a branch of computer science focused on developing systems that exhibit intelligent behavior. Broadly speaking, AI researchers aim to develop technologies that can ...Artificial intelligence, often referred to as AI, is a branch of computer science focused on developing systems that exhibit intelligent behavior. Broadly speaking, AI researchers aim to develop technologies that can think and act in a way that mimics human cognition and decision-making [1]. The foundations of AI can be traced back to early philosophical inquiries into the nature of intelligence and thinking. However, AI is generally considered to have emerged as a formal field of study in the 1940s and 1950s. Pioneering computer scientists at the time theorized that it might be possible to extend basic computer programming concepts using logic and reasoning to develop machines capable of “thinking” like humans. Over time, the definition and goals of AI have evolved. Some theorists argued for a narrower focus on developing computing systems able to efficiently solve problems, while others aimed for a closer replication of human intelligence. Today, AI encompasses a diverse set of techniques used to enable intelligent behavior in machines. Core disciplines that contribute to modern AI research include computer science, mathematics, statistics, linguistics, psychology and cognitive science, and neuroscience. Significant AI approaches used today involve statistical classification models, machine learning, and natural language processing. Classification methods are widely applicable to problems in various domains like healthcare, such as informing diagnostic or treatment decisions based on patterns in data. Dean and Goldreich, 1998, define ML as an approach through which a computer has to learn a model by itself from the data provided but no specification on the sort of model is provided to the computer. They can then predict values for things that are different from the values used in training the models. NLP looks at two interrelated concerns, the task of training computers to understand human languages and the fact that since natural languages are so complex, they lend themselves very well to serving a number of very useful goals when used by computers.展开更多
文摘Partial epilepsies, originating in a specific brain region, affect about 60% of adults with epilepsy. Temporal lobe epilepsy (TLE) is the most prevalent subtype within this category, often necessitating surgical intervention due to its refractoriness to antiepileptic drugs (AEDs). Hippocampal sclerosis, a common underlying pathology, often exacerbates the severity by introducing cognitive and emotional challenges. This review delves deeper into the cognitive profile of TLE, along with the risk factors for cognitive disorders, depression, and anxiety in this population.
基金National Natural Science Foundation of China,Nos.82003 729 (to Ying W),82022071 (to YiW)Natural Science Foundation of Shandong Province of China,No.ZR2020QH357 (to Ying W)Public Welfare Technology Research Program of Zhejiang Province,No.LGF20H09001 1 (to JF)。
文摘Cognitive impairment is the most common complication in patients with temporal lobe epilepsy with hippocampal scle rosis.There is no effective treatment for cognitive impairment.Medial septum cholinergic neurons have been reported to be a potential target for controlling epileptic seizures in tempo ral lobe epile psy.However,their role in the cognitive impairment of temporal lobe epilepsy remains unclear.In this study,we found that patients with temporal lobe epile psy with hippocampal sclerosis had a low memory quotient and severe impairment in verbal memory,but had no impairment in nonverbal memory.The cognitive impairment was slightly correlated with reduced medial septum volume and medial septum-hippocampus tra cts measured by diffusion tensor imaging.In a mouse model of chronic temporal lobe epilepsy induced by kainic acid,the number of medial septum choline rgic neurons was reduced and acetylcholine release was reduced in the hippocampus.Furthermore,selective apoptosis of medial septum cholinergic neurons mimicked the cognitive deficits in epileptic mice,and activation of medial septum cholinergic neurons enhanced hippocampal acetylcholine release and restored cognitive function in both kainic acid-and kindling-induced epile psy models.These res ults suggest that activation of medial septum cholinergic neurons reduces cognitive deficits in temporal lobe epilepsy by increasing acetylcholine release via projections to the hippocampus.
基金supported by STI2030-Major Projects,No.2022ZD0207600 (to LZ)the National Natural Science Foundation of China,Nos.821 71446 (to JY),U22A20301 (to KFS),32070955 (to LZ)+1 种基金Guangdong Basic and Applied Basic Research Foundation,No.202381515040015 (to LZ)Science and Technology Program of Guangzhou of China,No.202007030012 (to KFS and LZ)
文摘Epilepsy frequently leads to cognitive dysfunction and approaches to treatment remain limited.Although regular exercise effectively improves learning and memory functions across multiple neurological diseases,its application in patients with epilepsy remains controversial.Here,we adopted a 14-day treadmill-exercise paradigm in a pilocarpine injection-induced mouse model of epilepsy.Cognitive assays confirmed the improvement of object and spatial memory after endurance training,and electrophysiological studies revealed the maintenance of hippocampal plasticity as a result of physical exercise.Investigations of the mechanisms underlying this effect revealed that exercise protected parvalbumin interneurons,probably via the suppression of neuroinflammation and improved integrity of blood-brain barrier.In summary,this work identified a previously unknown mechanism through which exercise improves cognitive rehabilitation in epilepsy.
基金the National Science Foundation of China (No.91738201, 61971440)the Jiangsu Province Basic Research Project (No.BK20192002)+1 种基金the China Postdoctoral Science Foundation (No.2018M632347)the Natural Science Research of Higher Education Institutions of Jiangsu Province (No.18KJB510030)。
文摘In this paper,we investigate a spectrumsensing system in the presence of a satellite,where the satellite works as a sensing node.Considering the conventional energy detection method is sensitive to the noise uncertainty,thus,a temporal convolutional network(TCN)based spectrum-sensing method is designed to eliminate the effect of the noise uncertainty and improve the performance of spectrum sensing,relying on the offline training and the online detection stages.Specifically,in the offline training stage,spectrum data captured by the satellite is sent to the TCN deployed on the gateway for training purpose.Moreover,in the online detection stage,the well trained TCN is utilized to perform real-time spectrum sensing,which can upgrade spectrum-sensing performance by exploiting the temporal features.Additionally,simulation results demonstrate that the proposed method achieves a higher probability of detection than that of the conventional energy detection(ED),the convolutional neural network(CNN),and deep neural network(DNN).Furthermore,the proposed method outperforms the CNN and the DNN in terms of a lower computational complexity.
文摘Background: Mobility limitations and cognitive impairments which are common with ageing often coexist, causing a reduction in the levels of physical and mental activity and are prognostic of future adverse health events and falls. Consequently, multi-task training paradigms that simultaneously address both mobility and cognition benefit healthy ageing are important to consider in rehabilitation as well as primary prevention. Objectives: An exploratory RCT is being conducted to: a) describe the feasibility and acceptability of the study design and process, procedures, resources and management in two game-based dual-task training programs delivered in the community;b) to explore the lived experiences of the study participants who completed their respective exercise programs. A secondary objective is to obtain preliminary data on the therapeutic effectiveness of the two dual-task training programs. Methods: Thirty healthy older community dwelling participants aged 70 - 85 with previous history of falls will be recruited and randomized to either dual- task treadmill walking (experimental group) or dual-task recumbent bicycle (control group). Data analysis: The qualitative data will be analyzed by two investigators using a content analysis approach. For the quantitative data, outcome measures will be collected pre and post intervention and included measures to assess core balance, spatial-temporal gait variables, visual tracking and cognitive function, as well as, balance and gait analysis under dual-task conditions. Discussion: This research will demonstrate the feasibility of the dual-task training programs in the community, and demonstrate the system’s ability to improve targeted and integrated (dual-task) aspects of balance, mobility, gaze, and cognitive performance. A blended analysis of balance, mobility gaze and cognition will also contribute to a better understanding of the functional consequences of decline in physical and mental skills with age. Trial registration: This pilot clinical trial has been registered at ClinicalTrials.gov Protocol Registration System: NCT01940055.
文摘Artificial intelligence, often referred to as AI, is a branch of computer science focused on developing systems that exhibit intelligent behavior. Broadly speaking, AI researchers aim to develop technologies that can think and act in a way that mimics human cognition and decision-making [1]. The foundations of AI can be traced back to early philosophical inquiries into the nature of intelligence and thinking. However, AI is generally considered to have emerged as a formal field of study in the 1940s and 1950s. Pioneering computer scientists at the time theorized that it might be possible to extend basic computer programming concepts using logic and reasoning to develop machines capable of “thinking” like humans. Over time, the definition and goals of AI have evolved. Some theorists argued for a narrower focus on developing computing systems able to efficiently solve problems, while others aimed for a closer replication of human intelligence. Today, AI encompasses a diverse set of techniques used to enable intelligent behavior in machines. Core disciplines that contribute to modern AI research include computer science, mathematics, statistics, linguistics, psychology and cognitive science, and neuroscience. Significant AI approaches used today involve statistical classification models, machine learning, and natural language processing. Classification methods are widely applicable to problems in various domains like healthcare, such as informing diagnostic or treatment decisions based on patterns in data. Dean and Goldreich, 1998, define ML as an approach through which a computer has to learn a model by itself from the data provided but no specification on the sort of model is provided to the computer. They can then predict values for things that are different from the values used in training the models. NLP looks at two interrelated concerns, the task of training computers to understand human languages and the fact that since natural languages are so complex, they lend themselves very well to serving a number of very useful goals when used by computers.
